If you treat Model Context Protocol (MCP) like just another API, you’re going to miss the very risks attackers are most likely to exploit.
REST and GraphQL APIs have accumulated decades of security tooling. You probably already use API gateways, WAFs, and established IAM patterns to protect these endpoints. Security teams understand the threat landscape and know exactly which controls to implement.
MCP changes this equation entirely. It enables AI agents to share dynamic context, orchestrate multiple tools, and make autonomous decisions about resource access. These capabilities go far beyond what traditional API security was ever designed to handle.
The truth is, your existing security stack wasn’t built for agentic AI workflows where context flows continuously and agents act as independent, non-human identities. Securing MCP requires a fundamentally different approach than traditional API security.
A Quick Refresher on Traditional API Security
Traditional API security is straightforward: a client sends a request to an API endpoint, which routes to a backend service. You protect this flow with familiar controls:
- Authentication verifies the caller’s identity using OAuth 2.0, JWTs, or API keys.
- Authorization layers implement RBAC or ABAC to determine what authenticated callers can access.
- API Gateways enforce rate limits and validate request schemas.
- WAFs block common attack patterns like SQL injection.
This model works well because it assumes static, predictable flows. A developer codes an application to make specific API calls. The token represents a known identity and the request is structured and can be validated. These assumptions break down with MCP.
How MCP Differs Fundamentally from APIs
MCP introduces 4 architectural shifts that break the assumptions traditional API security relies on. Understanding these differences explains exactly why conventional controls fail to secure agent workflows.
| Characteristic | Traditional APIs | MCP |
| Data Exchange | Discrete requests/responses | Continuous context flow |
| Decision Making | Hardcoded, predictable calls | Autonomous, AI-driven choices |
| Workflow Pattern | Independent transactions | Orchestrated tool chains |
| Identity Model | User accounts or service principals | Non-human agent identities |
Dynamic Context Sharing
MCP agents carry an evolving context that includes conversation state, previous tool outputs, and sensitive data. This context flows continuously between agents and MCP servers, creating a persistent state that changes with every single interaction.
If an attacker compromises this context, they gain access not just to a single transaction, but to the entire chain of interactions and all the data the agent collected.
Traditional request validation can’t detect malicious context injection because the context isn’t a static payload, it’s a living, evolving data structure.
AI-Driven Decisioning
Agents using MCP make autonomous decisions about which tools to invoke and which resources to access. The agent interprets natural language instructions, decides which MCP servers to contact, and chains together tool calls to accomplish goals.
This autonomous behavior means you can’t predict or hardcode which resources an agent will access. Security controls must evaluate each decision in real time, understanding the agent’s intent and the current context.
Tool Orchestration
An agent might read from one MCP server, process that information, then write to another server, all as part of a single logical operation. Traditional APIs handle discrete transactions; MCP handles orchestrated sequences.
This creates lateral risk. If an attacker compromises one tool in the chain, they can potentially affect everything downstream.
Traditional API security treats each endpoint independently, but MCP workflow requires understanding how tools connect to prevent one compromised component from causing a broader breach.
Non-Human Identities as First-Class Actors
In MCP workflows, the client is typically an AI agent that uses the protocol to interact with tools and servers. The agent behaves as a non-human identity: it initiates requests, performs actions autonomously, and operates continuously without direct human involvement.
At the same time, the agent may inherit certain rights, scopes, or entitlements from the human who initiated the session, creating a blended identity. It acts as a machine but may carry human-derived authority. This hybrid model breaks the traditional separation between “user identity” and “service identity,” making it harder to reason about what the agent is allowed to do.
Because of this, security controls must verify the agent’s identity, understand the permissions it has, whether machine-originated, human-derived, or both, and ensure it operates within approved boundaries. API authentication patterns designed for human users or pre-provisioned service accounts don’t account for this blended, autonomous behavior and therefore fail to address the requirement.
Why Traditional Controls Fall Short
Each layer of traditional API security was built for static request-response architectures. When applied to MCP, these same controls leave critical gaps.
API gateways and WAFs: Excel at validating static request structures. They check schemas, enforce rate limits, and block known attack patterns. They can’t evaluate the authenticity of dynamic context flowing through MCP or understand whether an agent’s behavior falls within acceptable parameters.
OAuth 2.0: Authenticates the caller, but it doesn’t verify that an AI agent is trustworthy or that the context it carries hasn’t been tampered with. A valid OAuth token only proves the caller’s identity. It says nothing about the agent’s behavior, the integrity of its context, or whether its actions align with security policy.
Static secrets: Create even greater risks in MCP environments. API keys stored in agent configurations or embedded in context can leak through logs, error messages, or prompt injection attacks.
MCP’s dynamic nature means these secrets travel through multiple components, increasing exposure points. Traditional secrets management tools can’t eliminate this risk because they focus on storing and rotating credentials.
Monitoring Systems: These systems are designed for traditional APIs log discrete transactions. They capture request details, response codes, and error messages.
They don’t capture how context flows between agents and tools, how agents make decisions, or how tool chains execute. When something goes wrong in an MCP workflow, traditional logs lack the visibility to reconstruct what happened or identify where the security control failed.
Building the Right Security Model for MCP
Securing MCP requires an identity-first approach that treats every agent, server, and tool as a non-human identity. These identities need verification, policy-based access controls, and continuous monitoring.
Authentication
You can’t rely on static credentials provisioned weeks ago. You need real-time attestation that proves an agent is what it claims to be. In MCP workflows, that identity isn’t purely machine-based, agents often operate as a blended identity, combining their own non-human identity with certain rights or entitlements inherited from the human who initiated the interaction.
Authentication must therefore verify both the agent’s identity and the human-derived attributes it carries, and it must do so dynamically at runtime for both the agent and the MCP server. Only then can downstream policy decisions accurately reflect who, or what, is actually acting.
Authorization
Authorization must move beyond simple scopes or roles. MCP security demands context-aware policies that evaluate not just who the caller is, but what they’re trying to accomplish and what context they’re carrying.
These policies must adapt to agent behavior rather than enforcing rigid, pre-defined rules.
Auditability
Auditability is critical. Security teams need complete visibility into context flows, tool-orchestration sequences, and the decisions an agent made as it carried out a task. They must also be able to clearly distinguish what the agent did autonomously from what the human who initiated the workflow actually intended or authorized.
Reconstructing the full interaction chain, who triggered the flow, what blended identity the agent operated under, which tools it accessed, and how those access decisions were evaluated, is essential for incident response, governance, and regulatory compliance.
Runtime Enforcement
Runtime enforcement prevents attacks in progress. Instead of analyzing logs after an incident, MCP security solutions must detect and block malicious context injection, unauthorized tool access, or privilege escalation attempts as they occur.
Evaluate your MCP deployments against these criteria. Can you answer: What identity does this agent use? Which MCP servers can it access? What policies govern its tool access? Which security controls validate the context it carries?
If you can’t answer these questions, you’re exposed to the specific risks MCP introduces.
How Aembit Bridges the Gap
Securing MCP requires extending workload identity principles to a new class of actors: AI agents that operate autonomously but may also inherit permissions from the human who initiated the workflow. Aembit assigns every agent a verified non-human identity and supports a blended-identity model, binding the agent’s machine identity with human-derived entitlements when needed. This makes it possible to understand who is acting, what authority they carry and what they should be allowed to access.
Aembit’s MCP Gateway provides an enforcement point directly in front of MCP servers. Before an agent can invoke any tool, the gateway verifies its identity, evaluates the blended-identity attributes and applies policy. If access is allowed, the gateway performs a just-in-time token exchange, obtaining the appropriate short-lived credentials from a cloud or SaaS provider and forwarding them to the MCP server, without ever exposing those credentials to the agent itself. This eliminates long-lived API keys and prevents credential leakage through prompts, logs or model context.
Secretless access ensures agents never hold or manage credentials of any kind. Instead, Aembit authenticates the agent at runtime and enables access based on policy. This removes static secrets from MCP workflows and reduces the attack surface that autonomous agents would otherwise introduce.
Context-aware policy enforcement evaluates each tool invocation using real-time signals, including the agent’s identity, human-derived attributes, environment and posture. Policies can distinguish between what the agent is permitted to do and what the human who triggered it is entitled to do, enforcing least privilege across the blended identity. This prevents unauthorized tool access and constrains agent autonomy to approved boundaries.
Aembit also provides visibility and discovery across MCP flows. Security teams gain a clear, auditable record of which agents accessed which tools, under which identity and with which human-derived attributes. This makes it possible to separate human intent from agent action, reconstruct full interaction chains and support incident response and compliance review.
Finally, cross-cloud consistency ensures these controls apply uniformly whether MCP tools run in AWS, Azure, GCP or SaaS services. Aembit provides a single control plane for managing access policies, identity verification and auditability across diverse environments, matching the reality that MCP workflows span multiple clouds and service boundaries.
Securing MCP Requires New Thinking
Traditional API security tools solve yesterday’s problems. They protect request-response flows, validate static schemas, and enforce authentication patterns designed for human users. These controls remain important for conventional APIs, but they leave critical gaps in MCP environments where agents carry dynamic context, make autonomous decisions, and orchestrate complex tool chains.
Enterprises adopting MCP must rethink how they approach identity, context, and policy enforcement. Treating agents as glorified API clients misses the fundamental shifts that MCP introduces.
Security teams need purpose-built controls that understand non-human identities, validate context integrity, and enforce policies adapted to agentic workflows.
With Aembit, organizations can adopt MCP securely, without relying on outdated models that weren’t designed for agentic AI workflows.
